Autor: |
Qi Xin, Xiao Liang, Jin Yang, Xiaorui Wang, Fang Hu, Meng Jiang, Yijia Liu, Jin Gong, Yiwen Pan, Lijuan Liu, Jiao Xu, Yuxin Cui, Hongyu Qin, Han Bai, Yixin Li, Junpeng Ma, Chengsheng Zhang, Bingyin Shi |
Jazyk: |
angličtina |
Rok vydání: |
2024 |
Předmět: |
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Zdroj: |
Virology Journal, Vol 21, Iss 1, Pp 1-11 (2024) |
Druh dokumentu: |
article |
ISSN: |
1743-422X |
DOI: |
10.1186/s12985-024-02523-7 |
Popis: |
Abstract Background The metabolomic profiles of individuals with different clinical manifestations of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection have not been clearly characterized. Methods We performed metabolomics analysis of 166 individuals, including 62 healthy controls, 16 individuals with asymptomatic SARS-CoV-2 infection, and 88 patients with moderate (n = 42) and severe (n = 46) symptomatic 2019 coronavirus disease (COVID-19; 17 with short-term and 34 with long-term nucleic-acid test positivity). By examining differential expression, we identified candidate metabolites associated with different SARS-CoV-2 infection presentations. Functional and machine learning analyses were performed to explore the metabolites’ functions and verify their candidacy as biomarkers. Results A total of 417 metabolites were detected. We discovered 70 differentially expressed metabolites that may help differentiate asymptomatic infections from healthy controls and COVID-19 patients with different disease severity. Cyclamic acid and N-Acetylneuraminic Acid were identified to distinguish symptomatic infected patients and asymptomatic infected patients. Shikimic Acid, Glycyrrhetinic acid and 3-Hydroxybutyrate can supply significant insights for distinguishing short-term and long-term nucleic-acid test positivity. Conclusion Metabolomic profiling may highlight novel biomarkers for the identification of individuals with asymptomatic SARS-CoV-2 infection and further our understanding of the molecular pathogenesis of COVID-19. |
Databáze: |
Directory of Open Access Journals |
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